Hybrid Multi‐Strategy Improved Wild Horse Optimizer
Wild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four st...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-10-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200097 |
| _version_ | 1798027588397957120 |
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| author | Yancang Li Qiuyu Yuan Muxuan Han Rong Cui |
| author_facet | Yancang Li Qiuyu Yuan Muxuan Han Rong Cui |
| author_sort | Yancang Li |
| collection | DOAJ |
| description | Wild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four strategies to improve the optimization capability. The Halton sequence is used to initialize the foal population to make the population more diverse. The adaptive parameter TDR is improved to balance the global exploration and local exploitation. The simplex method is used to improve the worst position of the population. Wild horse escaping behavior is added to improve search efficiency and optimization accuracy. The main innovation strategies are the improvement of TDR and the addition of escaping behavior. To verify the effectiveness of the improved strategies, 12 benchmark test functions, CEC2017, and CEC2021 test functions are selected for simulation experiments. Mechanical design examples are added for optimization, and the optimization results are 16.61%, 1.65%, and 0.21% less than that of WHO. The results show that the improved algorithm has obvious advantages in convergence speed, accuracy, and stability. HMSWHO can be applied to more practical engineering optimization problems and provide new ideas for structural optimization methods. |
| first_indexed | 2024-04-11T18:55:02Z |
| format | Article |
| id | doaj.art-408805a238954df8abef38e5493a95f3 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-11T18:55:02Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-408805a238954df8abef38e5493a95f32022-12-22T04:08:13ZengWileyAdvanced Intelligent Systems2640-45672022-10-01410n/an/a10.1002/aisy.202200097Hybrid Multi‐Strategy Improved Wild Horse OptimizerYancang Li0Qiuyu Yuan1Muxuan Han2Rong Cui3College of Civil Engineering Hebei University of Engineering Handan 056038 ChinaCollege of Civil Engineering Hebei University of Engineering Handan 056038 ChinaSchool of Civil Engineering Tianjin University Tianjin 300354 ChinaCollege of Civil Engineering Hebei University of Engineering Handan 056038 ChinaWild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four strategies to improve the optimization capability. The Halton sequence is used to initialize the foal population to make the population more diverse. The adaptive parameter TDR is improved to balance the global exploration and local exploitation. The simplex method is used to improve the worst position of the population. Wild horse escaping behavior is added to improve search efficiency and optimization accuracy. The main innovation strategies are the improvement of TDR and the addition of escaping behavior. To verify the effectiveness of the improved strategies, 12 benchmark test functions, CEC2017, and CEC2021 test functions are selected for simulation experiments. Mechanical design examples are added for optimization, and the optimization results are 16.61%, 1.65%, and 0.21% less than that of WHO. The results show that the improved algorithm has obvious advantages in convergence speed, accuracy, and stability. HMSWHO can be applied to more practical engineering optimization problems and provide new ideas for structural optimization methods.https://doi.org/10.1002/aisy.202200097escaping behaviorHalton sequencemechanical optimizationnonlinear parametersimplex methodWild Horse Optimizer |
| spellingShingle | Yancang Li Qiuyu Yuan Muxuan Han Rong Cui Hybrid Multi‐Strategy Improved Wild Horse Optimizer Advanced Intelligent Systems escaping behavior Halton sequence mechanical optimization nonlinear parameter simplex method Wild Horse Optimizer |
| title | Hybrid Multi‐Strategy Improved Wild Horse Optimizer |
| title_full | Hybrid Multi‐Strategy Improved Wild Horse Optimizer |
| title_fullStr | Hybrid Multi‐Strategy Improved Wild Horse Optimizer |
| title_full_unstemmed | Hybrid Multi‐Strategy Improved Wild Horse Optimizer |
| title_short | Hybrid Multi‐Strategy Improved Wild Horse Optimizer |
| title_sort | hybrid multi strategy improved wild horse optimizer |
| topic | escaping behavior Halton sequence mechanical optimization nonlinear parameter simplex method Wild Horse Optimizer |
| url | https://doi.org/10.1002/aisy.202200097 |
| work_keys_str_mv | AT yancangli hybridmultistrategyimprovedwildhorseoptimizer AT qiuyuyuan hybridmultistrategyimprovedwildhorseoptimizer AT muxuanhan hybridmultistrategyimprovedwildhorseoptimizer AT rongcui hybridmultistrategyimprovedwildhorseoptimizer |